volatility of four widely traded cryptocurrencies, i.e., Bitcoin, Ethereum, Litecoin, and Ripple, by modeling volatility to … forecast the intraday price volatility. We evaluate the results under the MSE and MAE loss functions. Statistical analyses … the TGARCH(1,1), which are the best models for modeling the volatility process on out-of-sample data and have more …

breaks) in the volatility of financial time series. Comparative study of three techniques: ICSS, NPCPM and Cheng's algorithm … breaks in volatility, while Cheng's technique works well only when a single break occurs. …

This paper suggests how to quantify asymmetries in volatility spillovers that emerge due to bad and good volatility … stocks at the disaggregate level. Moreover, the spillovers of bad and good volatility are transmitted at different magnitudes …

individuals' sleep time exhibits both variability and volatility characterized by stationary autoregressive conditional … young children at home. Volatility is greater among parents with young children, slightly greater among men than women, but … independent of other demographics. A theory of economic incentives to minimize the dispersion of sleep predicts that higher …

financial investments has been debated in the literature. In this study, we compare the volatility of rates of return of …, it is important to model and quantify it. The conditional volatility models from the GARCH family and tail …

This paper addresses stock market volatility in Germany between 1991 and 2018. Through a GARCH model with leverage term …, an estimation of volatility in the DAX is provided. Such estimation is then plugged into a quantile regression model … where potential economic determinants are analyzed. The results suggest that stock market volatility in Germany reached its …

volatility índices (namely the originally created RTSVX and the new RVI that has replaced it), using daily data over the period …